SignLangGNN

When GNNs 💜 MediaPipe. This is a starter project where I tried to implement some traditional image classification problem i.e. the ASL sign language classification problem. The twist here is we used the graph generated from the hand images using mediapipe. And the graph I got, I extrated the {x, y, z} co-ordinates of the nodes and also the edge index for the connecteion and translated this image classification problem to a graph classiciation problem.

Project Structure

--------- Data
            |___ CSVs # containing the co-ordinates of per images
            |___ raw
                   |___ train.csv
                   |___ valid.csv
                   |___ test.csv 
            |___ ImageData
                   |___ asl_alphabet_test
                            |___ A/
                            |___ B/ 
                            ....
                            |___ space

                   |___ asl_alphabet_train
            |
            |___ Models # the GNN models
            |___ src
                   |__ dataset.py # pyg custom data
                   |__ train.py   # train loop
                   |__ utils.py   # different utility functions
            |
            |___ main.py # from data to train
            |___ run.py  # real time video visualization

I used PyTorch geometric and PyTorch for the project. To view the results in details head over to the IPYNB folder and see the first IPYNB file. To run this project first clone this repo using this command:

git clone https://github.com/Anindyadeep/SignLangGNN

After that run the main.py using this command. Other things will be managed automatically, provided al,l the essential libraries are installed.

python3 main.py

Initial Results

The traning and validation process went smooth as with a very simple base model it gave an train acc of 0.85 and validation acc of 0.86. It also provided an test acc of 0.84. The model was run for 8 epochs. The model also gets confused with some sort of examples and we can say that it currently suffers from adverserial attacks.

Improvements

These are the improvements we can do with this project:

1. Improved GNN models. We can make more robust and complex models and improve the performance.

2. Adding edge features. Some of the edge features like distance between two nodes and the angle between two nodes could produce some potential improvements to the performance of our model.

Future Works

Using Temporal Graph Neural Nets could make more robust and accurate model for this kind of problem. But for that we need temporal data like videos instaed of images, so that we could generate static temporal graphs and compute on them as a dynamic graph sequence problem.